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Partially Linear Models

  • Book
  • © 2000

Overview

Authors:
  1. Wolfgang Härdle

    1. Institut für Statistik und Ökonometrie, Humboldt-Universität zu Berlin, Berlin, Germany

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  2. Hua Liang

    1. Frontier Science & Technology Research Foundation, Harvard School of Public Health, Cestnut Hill, USA

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  3. Jiti Gao

    1. Department of Mathematics and Statistics, The University of Western Australia, Nedlands, Australia

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Part of the book series: Contributions to Statistics (CONTRIB.STAT.)

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Table of contents (6 chapters)

  1. Front Matter

    Pages i-x
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  2. Introduction

    • Wolfgang Härdle, Hua Liang, Jiti Gao
    Pages 1-18

  3. Estimation of the Parametric Component

    • Wolfgang Härdle, Hua Liang, Jiti Gao
    Pages 19-44

  4. Estimation of the Nonparametric Component

    • Wolfgang Härdle, Hua Liang, Jiti Gao
    Pages 45-54

  5. Estimation with Measurement Errors

    • Wolfgang Härdle, Hua Liang, Jiti Gao
    Pages 55-75

  6. Some Related Theoretic Topics

    • Wolfgang Härdle, Hua Liang, Jiti Gao
    Pages 77-126

  7. Partially Linear Time Series Models

    • Wolfgang Härdle, Hua Liang, Jiti Gao
    Pages 127-180

  8. Back Matter

    Pages 181-203
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Keywords

About this book

In the last ten years, there has been increasing interest and activity in the general area of partially linear regression smoothing in statistics. Many methods and techniques have been proposed and studied. This monograph hopes to bring an up-to-date presentation of the state of the art of partially linear regression techniques. The emphasis is on methodologies rather than on the theory, with a particular focus on applications of partially linear regression techniques to various statistical problems. These problems include least squares regression, asymptotically efficient estimation, bootstrap resampling, censored data analysis, linear measurement error models, nonlinear measurement models, nonlinear and nonparametric time series models.

Authors and Affiliations

  • Institut für Statistik und Ökonometrie, Humboldt-Universität zu Berlin, Berlin, Germany

    Wolfgang Härdle

  • Frontier Science & Technology Research Foundation, Harvard School of Public Health, Cestnut Hill, USA

    Hua Liang

  • Department of Mathematics and Statistics, The University of Western Australia, Nedlands, Australia

    Jiti Gao

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